HIGHLIGHTS
- who: Francesco Armando Di Bello from the Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy have published the paper: Efficiency Parameterization with Neural Networks, in the Journal: (JOURNAL)
- what: The authors propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. The authors show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy-flavor tagging classifiers in HEP experiments including for processes on which it was not trained. The . . .
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